Principal Data Engineer

Role Overview

Join Salesforce as a Principal Data Engineer (full remote). You will design and implement a robust data model and scalable end-to-end data architecture integrating core B2B systems to support marketing performance measurement, customer segmentation, targeting, and personalization.

Key Missions / Responsibilities

• Design & implement data models integrating core B2B systems, including:
  • Snowflake
  • Salesforce Data 360
  • Multiple Salesforce orgs
  • Informatica MDM
  • Amazon data lakes
• Evolve scalable end-to-end data architecture and define standards for:
  • Data modeling
  • Ingestion frameworks and pipelines
  • Data quality
• Translate business needs for marketing analytics and personalization into precise data requirements and model designs.
• Partner with Data/Application Architects and lead technical design discussions, aligning stakeholders on trade-offs.
• Support ML feature engineering by designing data models for feature stores and feature registries, considering impacts on freshness, training pipelines, and real-time inference.
• Implement Data Mesh principles: domain-owned data products, clear SLAs and documentation, and federated governance balancing central standards with domain autonomy.
• Partner with Data Governance teams to ensure data models are compliant, secure, and integrated with the enterprise data catalog.
• Work across marketing data domains (campaign management, CRM, web analytics, attribution/marketing mix modeling, propensity modeling, forecasting, optimization), including modeling slowly changing dimensions and historical tracking.

Requirements

• 10+ years hands-on experience in data modeling, data architecture, or database design.
• 5+ years designing and implementing large-scale Enterprise Data Warehouses.
• Expert-level dimensional modeling (Star/Snowflake schemas) for BI, reporting, and ML feature engineering.
• Mastery of major modeling methodologies and trade-offs, including:
  • 3NF (applications)
  • Data Vault (integration)
  • Star/Snowflake (data science / analytics)
• Advanced SQL plus strong DDL/DML skills optimized for Snowflake.
• Deep, hands-on expertise in Snowflake, building and optimizing models on a cloud-native data warehouse.
• Experience with ETL/ELT tools (e.g., dbt, Fivetran) and cloud services (AWS/GCP/Azure).
• Deep experience with Master Data Management (golden records, hierarchies) integrated with operational/analytical systems (e.g., Informatica MDM).
• Exceptional communication: lead technical discussions and explain complex concepts and implementation trade-offs to both technical and business stakeholders.

Nice-to-haves / Additional Signals

• Experience designing data models supporting ML workloads such as attribution models, lead scoring, and propensity models.
• Familiarity with Salesforce Data 360 for enterprise data model objects (designing, deploying, managing).
• Master’s or Ph.D. in Computer Science, Information Systems, or a related quantitative field.

Education

• Master’s or Ph.D. in Computer Science, Information Systems, or a related quantitative field.